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Due to the increasing diversity of organic contaminants discharged into anoxic water environments, reactivity prediction is necessary for chemical persistence evaluation for water treatment and risk assessment purposes. Almost all quantitative structure activity relationships (QSARs) that describe rates of contaminant transformation apply only to narrowly-defined, relatively homogenous families of reactants (e.g., dechlorination of alkyl halides). In this work, we develop predictive models for abiotic reduction of 60 organic compounds with diverse reducible functional groups, including nitroaromatic compounds (NACs), aliphatic nitro-compounds (ANCs), aromatic N-oxides (ANOs), isoxazoles (ISXs), polyhalogenated alkanes (PHAs), sulfoxides and sulfones (SOs), and others. Rate constants for their reduction were measured using a model reductant system, Fe(II)-tiron. Qualitatively, the rates followed the order NACs > ANOs ≈ ISXs ≈ PHAs > ANCs > SOs. To develop QSARs, both conventional chemical descriptor-based and machine learning (ML)-based approaches were investigated. Conventional univariate QSARs based on a molecular descriptor ELUMO (energy of the lowest-unoccupied molecular orbital) gave good correlations within classes. Multivariate QSARs combining ELUMO with Abraham descriptors for physico-chemical properties gave slightly improved correlations within classes for NCs and NACs, but little improvement in correlation within other classes or among classes. The ML model obtained covers reduction rates for all classes of compounds and all of the conditions studied with the prediction accuracy similar to those of the conventional QSARs for individual classes (r2 = 0.41–0.98 for univariate QSARs, 0.71–0.94 for multivariate QSARs, and 0.83 for the ML model). Both approaches required a scheme for a priori classification of the compounds for model training. This work offers two alternative modeling approaches to comprehensive abiotic reactivity prediction for persistence evaluation of organic compounds in anoxic water environments.

A floating production system is exposed to many sea conditions over its lifetime. Consequently, fatigue design of a riser system is challenging, owing to the need to consider the long-term wave condition, characterized by the joint statistics of the significant wave height and peak wave period. The problem becomes computationally intractable if dynamic analysis is performed in the time domain, which is often necessary due to nonlinearities. This paper outlines a new efficient approach for long-term riser fatigue analysis, in conjunction with time domain simulations. The time series of an irregular wave elevation is widely simulated as the sum of regular wave components with random amplitudes and phase angles. Importance sampling is the basis of the proposed approach; however, it is not exploited for variance reduction. Instead, this technique allows one to simulate the wave amplitudes from a distribution different from the original one. This implies that following a dynamic analysis, the results for many sea states can be obtained by simply altering the importance sampling weighting function. The proposed approach is enhanced by a succession of additional techniques to reduce the sampling variability. Since importance sampling is not an approximate method, but an alternative means of simulation, the predicted long-term fatigue damage is unbiased. The standard error is estimated via bootstrapping.

Diarylide yellow pigments/modified SiO 2 core–shell hybrid composite particles were fabricated via a newly developed two-step procedure. The surface of diarylide yellow pigments was coated with SiO 2 by the hydrolysis of Na 2 SiO 3 . The obtained particles were then modified with in situ generated 3-aminopropylsilanetriol or succinic acid to form composite particles containing amino or carboxyl groups. The FT-IR spectra, TGA and TEM showed that the particles had a core–shell structure, the SEM suggested that the surface morphology of the composite particles was smooth, and the XPS and zeta potential measurement indicated that the composite particles had a high charge load. The obtained composite particles have been applied as electrophoretic particles in the electronic paper, which demonstrated that the newly developed procedure is an effective way to produce various organic/inorganic composite particles used for electrophoretic displays.